Informatics Tools for High-throughput Analysis of Cancer Mutations

用于癌症突变高通量分析的信息学工具

• 批准号: 8606625
• 负责人: Rachel Karchin
• 金额: $ 29万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-17 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8606625
• 关键词: Address; Binding Sites; Bioinformatics; Biological; Cancer Center; Cancer Prognosis; Categories; Classification; Collaborations; Communities; Computer Analysis; Computer software; Country; Data; Databases; Development; Diagnostic; Disease; Doctor of Medicine; Ensure; Gene Frequency; Gene Mutation; Genes; Genetic; Genetic Polymorphism; Genomics; Heterogeneity; Histocompatibility Testing; Housing; Image; Imagery; Informatics; Internet; Letters; Machine Learning; Malignant Neoplasms; Maps; Methods; Missense Mutation; Molecular; Mutagenesis; Mutate; Mutation; Mutation Analysis; Occupations; Pathway Analysis; Pattern; Pharmaceutical Preparations; Population; Positioning Attribute; Privacy; Production; Proteins; Publications; Qualifying; RNA Splicing; Research Infrastructure; Research Personnel; Resources; Roentgen Rays; Scientist; Secure; Site; Source; Structure; Technology; The Cancer Genome Atlas; Transcript; Translations; Tumor Tissue; Untranslated Regions; Update; Variant; Work; anticancer research; base; cancer cell; cancer classification; cancer genome; cancer genomics; cancer therapy; cohort; data exchange; design; exome; exome sequencing; experience; high throughput analysis; improved; insertion/deletion mutation; insight; interest; microbial alkaline proteinase inhibitor; next generation sequencing; novel strategies; prognostic; protein function; protein structure; public health relevance; software development; tool; tumor; user-friendly; web interface; web services

PROJECT SUMMARY Large tumor exome sequencing projects have identified a very large number of mutations whose cancer relevance is not yet understood. To begin to address this need, our team has produced two web applications for high-throughput computational analysis of cancer mutations: the Cancer-Related Analysis of VAriants Toolkit (CRAVAT) and the Mutation Position Imaging Toolbox (MuPIT). CRAVAT accepts millions of mutations in a single batch upload and maps mutations from genomic coordinates to annotated transcripts and proteins. MuPIT currently accepts batch uploads of up to 2500 SNVs and maps from genomic coordinates onto X-ray crystal structures of proteins from Protein Data Bank (PDB). We propose to combine and harden CRAVAT and MuPIT into a single web application, in which we will substantially improve the tools, user interface, software infrastructure, integration with external data resources and tools used by the community, and support for protected data. The scope of all tools in the web application will be broadened to handle analysis of the full range of small-scale mutation consequence types found in cancer exomes. CRAVAT analysis identifies mutations most likely to have deleterious impact on protein function and those that are most likely to confer a selective advantage to cancer cells (drivers), using classifiers developed by our team. Classifier scores are supplemented with annotations, including population allele frequencies, previous occurrence in tumor tissue types, and gene functional categories, enabling filtering (e.g. removing polymorphisms) and prioritization. Gene-level annotation and scoring, by aggregation of classifier scores from mutations in a cohort is also provided. MuPIT maps mutations from genomic positions onto to protein structures and provides interactive viewing of mutations in the context of protein structure, and in relation to a variety of annotations. To enable prioritization of interesting mutations and genes, the application provides a preview describing each structure and all available annotations (e.g., binding sites, experimental mutagenesis results, polymorphic and disease- associated variants that have been previously documented). After selecting a PDB of interest, the user is led to an interactive visualization page. An enhanced Jmol applet displays all SNVs mapped onto the structure. Frequently, many SNVs in the input list can be mapped onto a single structure, revealing clustering patterns around key functional sites. Based only on word-of-mouth, since the debut of the two applications in August 2012, CRAVAT has been utilized by 129 unique users from 39 countries, and it has analyzed 1,136 submitted jobs, totaling 27.9 million mutations. MuPIT has been utilized by 242 unique users from 25 countries, with 720 submitted jobs. (Source: Google Analytics).

项目总结 大型肿瘤外显子组测序计划已经确定了非常大量的突变，其癌症 相关性还不是很清楚。为了开始解决这一需求，我们的团队开发了两个Web应用程序 用于癌症突变的高通量计算分析：变异的癌症相关分析 工具包(Cravat)和突变位置成像工具箱(MuPIT)。Cravat接受数百万种突变 在单个批次中，上传并映射从基因组坐标到带注释的转录本和蛋白质的突变。 MuPIT目前接受从基因组坐标到X射线的最多2500 SNV和地图的批量上传 蛋白质数据库(PDB)中蛋白质的晶体结构我们建议将领带合并并硬化 和MuPIT整合为单一的Web应用程序，在其中我们将大幅改进工具、用户界面、 软件基础设施、与社区使用的外部数据资源和工具的集成，以及支持 用于受保护的数据。Web应用程序中的所有工具的范围将扩大到处理完整的 在癌症外显子中发现的一系列小规模突变后果类型。 Cravat分析确定最有可能对蛋白质功能产生有害影响的突变和那些 使用我们开发的分类器，最有可能赋予癌细胞(驱动程序)选择优势的人 一队。分类器分数由注释补充，包括群体等位基因频率，以前 出现在肿瘤组织类型和基因功能类别中，从而实现过滤(例如，移除 多态)和优先顺序。基因级别的注释和评分，通过聚合来自 还提供了队列中的突变。 MuPIT将突变从基因组位置映射到蛋白质结构，并提供交互式查看 蛋白质结构背景下的突变，以及与各种注释有关的突变。要启用 对感兴趣的突变和基因进行优先排序，该应用程序提供了描述每个结构的预览 和所有可用的注释(例如，结合位点、实验突变结果、多态和疾病- 先前已记录的相关变体)。在选择感兴趣的PDB之后，用户被引导至 交互式可视化页面。增强的JMOL小程序显示映射到结构上的所有SNV。 通常，输入列表中的多个SNV可以映射到单个结构上，从而显示出集群模式 围绕关键功能部位。 仅基于口碑，自2012年8月两个应用程序亮相以来，领带一直是 它被来自39个国家的129个独立用户使用，并分析了1136个提交的职位，总计2790万个 突变。来自25个国家的242个独立用户使用了MuPIT，提交了720个工作。(来源： 谷歌分析)。